Both are Alibaba models. Qwen3.6 27B is the newer, generally stronger default; reach for Qwen3 235B A22B when a specific cost or latency profile matters more than the latest capabilities.
Qwen3 235B A22B and Qwen3.6 27B are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Qwen3.6 27B is a dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Qwen3.6 27B is the newer model by about 9 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Qwen3 235B A22B
Qwen3.6 27B
Provider
Alibaba (China)
Alibaba (China)
Released
July 21, 2025
April 22, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B — Qwen3 235B A22B lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; Qwen3.6 27B does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B — Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Qwen3.6 27B does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B — Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Qwen3.6 27B does not.
The best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size: Qwen3.6 27B — Qwen3 235B A22B is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised: Qwen3.6 27B — Qwen3 235B A22B is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0): Qwen3.6 27B — A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token — and it is the newer of the two.
Which should you pick?
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B — It is specifically built for that.
Anyone whose priority is the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size: Qwen3.6 27B — That is its strongest area.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs are real: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.6 27B: where it fits
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Released April 22, 2026 by Alibaba, it is built for the best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size, dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised, far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0), and dense models fine-tune far more predictably than mixture-of-experts models do.
Its trade-offs: every parameter fires on every token, so it is slower and costlier per token than the sparse 35B, hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter, and its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Because Qwen3 235B A22B and Qwen3.6 27B come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.6 27B is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.6 27B and drop down only with a concrete reason.
Frequently asked questions
Is Qwen3 235B A22B or Qwen3.6 27B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) while Qwen3.6 27B leans toward the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3 235B A22B or Qwen3.6 27B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3 235B A22B to Qwen3.6 27B?
Since both are Alibaba models, the newer one (Qwen3.6 27B) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3 235B A22B or Qwen3.6 27B?
Qwen3.6 27B — released April 22, 2026, about 9 months after Qwen3 235B A22B.
Qwen3 235B A22B vs Qwen3.6 27B
Alibaba · China | Alibaba · China · Updated June 2026
Quick verdict
Both are Alibaba models. Qwen3.6 27B is the newer, generally stronger default; reach for Qwen3 235B A22B when a specific cost or latency profile matters more than the latest capabilities.
Qwen3 235B A22B and Qwen3.6 27B are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Qwen3.6 27B is a dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Qwen3.6 27B is the newer model by about 9 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Qwen3 235B A22B
Qwen3.6 27B
Provider
Alibaba (China)
Alibaba (China)
Released
July 21, 2025
April 22, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B
Qwen3 235B A22B lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; Qwen3.6 27B does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B
Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Qwen3.6 27B does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B
Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Qwen3.6 27B does not.
The best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size
Qwen3.6 27B
Qwen3 235B A22B is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised
Qwen3.6 27B
Qwen3 235B A22B is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0)
Qwen3.6 27B
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token — and it is the newer of the two.
Which should you pick?
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B
It is specifically built for that.
Anyone whose priority is the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size
→ Qwen3.6 27B
That is its strongest area.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs are real: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.6 27B: where it fits
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Released April 22, 2026 by Alibaba, it is built for the best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size, dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised, far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0), and dense models fine-tune far more predictably than mixture-of-experts models do.
Its trade-offs: every parameter fires on every token, so it is slower and costlier per token than the sparse 35B, hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter, and its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Because Qwen3 235B A22B and Qwen3.6 27B come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.6 27B is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.6 27B and drop down only with a concrete reason.
Want both Qwen3 235B A22B and Qwen3.6 27B without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Qwen3 235B A22B or Qwen3.6 27B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) while Qwen3.6 27B leans toward the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Qwen3 235B A22B or Qwen3.6 27B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3 235B A22B to Qwen3.6 27B?
Since both are Alibaba models, the newer one (Qwen3.6 27B) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3 235B A22B or Qwen3.6 27B?
Qwen3.6 27B — released April 22, 2026, about 9 months after Qwen3 235B A22B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.